Abstract: An operator locking tool allows a user to define a lock profile for one or more operators in a streaming application. The lock profile preferably specifies lock criteria and one or more corresponding lock actions that are taken when the lock criteria is satisfied. The lock criteria can include operator performance, resource utilization, events, and user-defined triggers. The lock actions can include blocking tuple entry, blocking tuple exit, halting tuple processing, processing high-priority tuples while not processing other tuples, allowing tuples to exit an operator only when specified exit criteria is satisfied, and enabling bypass of an operator. Locking can be done for individual operators or for a group of operators.

Abstract: A streams analysis tool allows a user to define one or more buckets according to a specified tuple collection criteria for each bucket. The specified tuple collection criteria for each bucket defines some way to distinguish one data tuple from another. The specified tuple collection criteria for each bucket is therefore used to distinguish data tuples that satisfy the specified tuple collection criteria from data tuples that do not satisfy the specified tuple collection criteria. When a data tuple satisfies the specified tuple collection criteria for a bucket, the data tuple is stored in the bucket. In addition, data tuples preceding or succeeding the data tuple may also be stored in the bucket, as determined by the specified tuple collection criteria. The data tuples in each bucket are analyzed, and based on the analysis a streams manager can change how future data tuples are processed by the streaming application.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving, by a database management system (DBMS), a query for execution, computing, by a query governor, a first resource consumption value for executing a first portion of the received query against a plurality of data tuples in an operator graph of a distributed application, and upon determining that the first resource consumption value does not exceed a first threshold value, executing the query by operation of one or more computer processors.

Abstract: A streams analysis tool allows a user to define one or more buckets according to a specified tuple collection criteria for each bucket. The specified tuple collection criteria for each bucket defines some way to distinguish one data tuple from another. The specified tuple collection criteria for each bucket is therefore used to distinguish data tuples that satisfy the specified tuple collection criteria from data tuples that do not satisfy the specified tuple collection criteria. When a data tuple satisfies the specified tuple collection criteria for a bucket, the data tuple is stored in the bucket. In addition, data tuples preceding or succeeding the data tuple may also be stored in the bucket, as determined by the specified tuple collection criteria. The data tuples in each bucket are analyzed, and based on the analysis a streams manager can change how future data tuples are processed by the streaming application.

Abstract: Profiling data characterizing a data streaming application is used to project changes to a relational database resulting from current in-flight streamed data. Preferably, the streaming application produces tuples which are entered into the relational database. Trace data is collected during previous execution of the streaming application to construct operator graph profile data showing likely paths of tuples through multiple processing elements of the streaming application. Responsive to a query, agent(s) residing within the computer system(s) supporting the streaming application query in-flight tuples in one or more buffers of the streaming application. The responses to the agent queries are analyzed using the operator graph profile data to project tuples which will be output to the database. Projected changes to the database may alternatively be used for other purposes, e.g., creating database metadata structures; reorganizing data inserts; regulating query governors; and/or updating database statistics.

Abstract: Disclosed aspects relate to managing a database management system (DBMS) using a set of stream computing data derived from a stream computing environment. The set of stream computing data which indicates a set of stream computing environment statistics may be collected with respect to the stream computing environment. A proactive database management operation may be determined for performance with respect to the DBMS based on the set of stream computing data which indicates the set of stream computing environment statistics. The proactive database management operation may be performed to manage the DBMS using the set of stream computing data.

Abstract: Disclosed aspects relate to managing a database management system (DBMS) using a set of stream computing data derived from a stream computing environment. The set of stream computing data which indicates a set of stream computing environment statistics may be collected with respect to the stream computing environment. A proactive database management operation may be determined for performance with respect to the DBMS based on the set of stream computing data which indicates the set of stream computing environment statistics. The proactive database management operation may be performed to manage the DBMS using the set of stream computing data.

Abstract: Disclosed aspects relate to managing a stream computing environment using a projected database object. A set of realized data of a realized database object of a database management system (DBMS) may be compared with a set of projected data of a projected database object of the DBMS. The set of realized data of the realized database object and the set of projected data of the projected database object may be compared with respect to the DBMS that relates to the stream computing environment. An outlier subset of the set of projected data may be identified based on comparing the set of realized data with the set of projected data. Based on the outlier subset of the set of projected data, an outlier response action may be executed in the stream computing environment.

Abstract: Disclosed aspects relate to managing a database management system (DBMS) using a set of stream computing data derived from a stream computing environment. The set of stream computing data which indicates a set of stream computing environment statistics may be collected with respect to the stream computing environment. A proactive database management operation may be determined for performance with respect to the DBMS based on the set of stream computing data which indicates the set of stream computing environment statistics. The proactive database management operation may be performed to manage the DBMS using the set of stream computing data.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be compiled.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be compiled.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be established.

Abstract: Disclosed aspects relate to operation efficiency management in a shared pool of configurable computing resources. A first set of processing operations of a first application may be detected. A second set of processing operations of a second application may be detected. The first set of processing operations of the first application may be compared with the second set of processing operations of the second application. A substantial match of the first and second processing operations of the first and second applications may be determined. A single set of processing operations for both the first and second applications may be compiled.

Abstract: A streams manager includes a missing data mechanism that allows operators to forward data tuples that have missing derived data to a next operator in a streaming application. One or more new threads are created to continue processing the missing derived data. Once the processing for the missing derived data is complete, the derived data is reunited with the data tuple. The data tuple with missing derived data can travel until it reaches an operator that requires the missing derived data. The data tuple is then placed in a waiting area for the operator. Once the missing derived data is added to the data tuple in the waiting area, the data tuple can be processed by the operator that required the derived data.

Abstract: Disclosed aspects relate to window management in a stream computing environment. An indication of congestion may be detected with respect to the stream computing environment. Based on the indication of congestion, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.

Abstract: Disclosed aspects relate to window management in a stream computing environment. An indication of congestion may be detected with respect to the stream computing environment. Based on the indication of congestion, a set of window configurations in the stream computing environment may be determined. In response to determining the set of window configurations in the stream computing environment, the set of window configurations may be established in the stream computing environment.

Abstract: Techniques are described for managing the execution of one or more groups of queries. Embodiments of the present disclosure may generally receive a group of queries to be executed against a database. Embodiments also determine, based on one or more attributes of the group of queries, an expected amount of resources that will be used in executing the group of queries against the database. Embodiments further schedule one or more queries of the group of queries for execution against the database based on the expected amount of resources to be used for the group of queries.

Abstract: Disclosed aspects relate to managing a stream computing environment using a projected database object. A set of realized data of a realized database object of a database management system (DBMS) may be compared with a set of projected data of a projected database object of the DBMS. The set of realized data of the realized database object and the set of projected data of the projected database object may be compared with respect to the DBMS that relates to the stream computing environment. An outlier subset of the set of projected data may be identified based on comparing the set of realized data with the set of projected data. Based on the outlier subset of the set of projected data, an outlier response action may be executed in the stream computing environment.

Abstract: Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.

Abstract: Profiling data characterizing a data streaming application is used to predict data which will need to be retrieved by a processing element during execution of the data streaming application. Data is retrieved responsive to the prediction, in advance of actual demand by the processing element which requires it. Prediction may be based at least in part on upstream tuple contents, and could include other historical data retrieval patterns. In some embodiments, retrieval of predicted data may be delayed so that data is retrieved just in time.